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Comparative Study for Classification Methods to Predict and Detect Epilepsy Seizure 5

Comparative Study for Classification Methods to Predict and ...

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5th IEEE Middle East and Africa Conference on Biomedical engineering, MECBME 2020

作者： El Halabi, Nashaat Abi Zeid Daou, Roy Achkar, Roger Hayek, Ali Boercsoek, Josef American University of Science and Technology Department of Computer and Communications Engineering Faculty of Engineering Beirut Lebanon Faculty of Public Health Biomedical Technologies Department Jounieh Lebanon American University of Science and Technology Department of Computer and Communications Engineering Faculty of Engineering Beirut Lebanon Computer Science University of Kassel Department of Computer Architecture and System Programming Faculty of Electrical Engineering Germany Computer Science University of Kassel Department of Computer Architecture and System Programming Faculty of Electrical Engineering Germany

ISBN: (纸本)9781728123585

Epilepsy is a medical problem that tackles lots of patients. It limits the life activity of such patients due to the seizures that occur anytime and anywhere. Thus, creating a monitoring system that could make their life easier and allow them to perform daily life activities as safe as possible has been studied. This paper is a continuity for a project where a system monitors epileptic patients using a set of wearable sensors connected to several processing units, the main sensors are Electroencephalogram (EEG) for the electrical activity of the brain, electrocardiogram (ECG) for the electrical activity of the heart, and accelerometers to monitor the posture of the patient, features extracted from EEG and ECG signals are used for prediction/detection of seizures. Thus, the objective of this paper is to present a comparative study in order to choose the appropriate classification method for seizure prediction and detection. Five classification methods were studied: Decision Trees, Discriminant Analysis, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Ensemble Learning. For each of the classification methods, the accuracy, the specificity, the sensitivity, the precision, the false detection rate and the Fmeasure were studied and compared. The results for seizure prediction system show that the highest accuracy, specificity and F-Measure, and lowest false detection rate were achieved with Bagged Trees Ensemble Learning, the highest sensitivity and precision were achieved with Quadratic Support Vector Machine. However, for seizure detection system, the highest accuracy and F-Measure were achieved with Linear Support Vector, the highest sensitivity and specificity were achieved with Fine Gaussian Support Vector Machine, and the highest precision and lowest false detection rate were achieved with Subspace Discriminant Ensemble Learning. © 2020 IEEE.

关键词： Support vector machines

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Proactive control of rolling stock with traction asynchronous electric motors 2

Proactive control of rolling stock with traction asynchronou...

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2nd IEEE KhPI Week on Advanced Technology, KhPI Week 2021

作者： Dmitrienko, Valery Noskov, Valentin Zakovorotniy, Alexander Mezentsev, Nikolay Leonov, Sergey Gasanov, Magomediemin National Technical University Kharkiv Polytechnic Institute Computer Engineering and Programming Department Kharkov Ukraine Vice-rector for Scientific and Pedagogical Work National Technical University Kharkiv Polytechnic Institute Kharkiv Ukraine

ISBN: (纸本)9781665412056

Proactive management research continues to evolve over time. At the same time, they find applications in various fields of industry. Main approaches of complex objects managing are considered in this article. It is shown that today it is advisable to use approaches related to proactive control to control rolling stock with traction asynchronous electric motors. While using a mathematical model which describes the movement of a diesel train with three carriages, its application for the implementation of proactive control is demonstrated. Approaches related to the identification of the parameters of an object during its operation are proposed in order to clarify the parameters of the existing model in the control system. In this case, it is proposed to identify the parameters of the object at two stages: at the first (lower) level, to determine the parameters which are associated with traction asynchronous electric motors, and at the second (upper) level, the parameters which are associated with the movement of the train. The basic relationships which describe the identification process are given. © 2021 IEEE.

关键词： Parameter estimation

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Modelling the Process of Determining the Tourist Demand Based on Fishbein's Multi-Attribute Model 15

Modelling the Process of Determining the Tourist Demand Base...

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15th IEEE International Scientific and Technical Conference on computer Sciences and Information Technologies, CSIT 2020

作者： Hovorushchenko, Tetiana Glukhov, Vladyslav Hovorushchenko, Olha Computer Engineering and System Programming Department Khmelnytskyi Ukraine Khmelnytskyi Lyceum No. 17 Khmelnytskyi Ukraine

ISBN: (纸本)9781728174433

The purpose of this paper is automating the analysis of tourist demand by developing a web-service for surveying tourists (both real and potential) and for presenting the survey results in the form of determining the tourist demand. The model of the process of determining the tourist demand based on Fishbein's multi-attribute model has been developed. The most important attributes, by which tourists evaluate their travels, and the relative importance of such attributes are identified. Rules for translating the thoughts and ratings of the user into an acceptable for the developed model 7-point bipolar scale have been developed. The web-service, which provides automating the calculation of tourist attraction of different countries, has been developed. © 2020 IEEE.

关键词： Web services

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The Study of the Dispersion and Phase Velocity of Love Waves 41

The Study of the Dispersion and Phase Velocity of Love Waves

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41st IEEE International Conference on Electronics and Nanotechnology, ELNANO 2022

作者： Porkuian, Olga Morkun, Volodymyr Morkun, Natalia Pikilnyak, Andrii Gaponenko, Alena Gaponenko, Irina Bobrov, Evhen Vesnin, Artem Volodymyr Dahl East Ukrainian National University Department of Programming and Mathematics Severodonetsk Ukraine Kryvyi Rih National University Department of Automation Computer Science and Technology Kryvyi Rih Ukraine Kryvyi Rih National University Department of Mechanical Engineering Kryvyi Rih Ukraine Kryvyi Rih National University R&d Department Kryvyi Rih Ukraine Kryvyi Rih National University Department of Motor Transport Kryvyi Rih Ukraine

ISBN: (数字)9781665469227

ISBN: (纸本)9781665469234

The research results of the Love wave propagation in the semi-space contact area and in a thin layer are presented. The dependences, which allow analyzing the effect of various conditions on the Love wave dispersion, as well as the dependence of the phase velocity on various parameters: the thickness of the layer, which is coated in semi-space;the relationship between the material characteristics of the media (densities, elastic moduli) are defined. The influence of film thickness on the value of phase velocity at a given frequency, but at different ratios between the density of the media are studied. © 2022 IEEE.

关键词： Dispersion (waves)

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Statistical Analysis of Capacity Loss for Stored Batteries

Statistical Analysis of Capacity Loss for Stored Batteries

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Luminita–Mirela Constantinescu Bogdan–Adrian Enache Violeta–Georgiana Dogaru Teodor Iulian Voicila Irina Vilciu Silvia–Carmen Vasile Electronics Computer and Electrical Engineering Department University of Pitesti Pitesti Romania Measurements Electrical Apparatus and Static Converters Department University Politehnica of Bucharest Bucharest Romania Electrical Engineering Faculty University Politehnica of Bucharest Bucharest Romania Programming and Planning Department for the Teritorial Dispatch Center Bucharest Transelectrica S.A Bucharest Romania

Lithium-ion battery production is generally geared towards current demand from the main sectors: electric vehicles, consumer electronics, and energy storage. These sectors typically require the batteries to be as fresh as possible to ensure maximum lifespan and performance. Still, sometimes manufacturers work with buffer stocks and deliver batteries from different production series. In this context, the present paper examines stored batteries' capacity loss, employing an exhaustive statistical study. This study aims to establish if the capacity loss is statistically significant and can lead to battery pack unbalances.

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Advanced IOT Traffic Light Control System

Advanced IOT Traffic Light Control System

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International Conference on Advances in Computational Tools for engineering Applications, ACTEA

作者： Martin Abou Hamad Antonio El Hajj Roy Abi Zeid Daou Ali Hayek Gaby Abou Haidar Biomedical Technologies Department Faculty of Public Health Lebanese German University Lebanon MART Learning Education and Research Center Lebanon Department of Computer Architecture and System Programming University of Kassel Kassel Germany Engineering Faculty American University of Science and Technology Zahle Lebanon

For several years, traffic congestion has been a major problem in big cities where the number of cars and different means of transportation has been increasing significantly. The problem of congestion is becoming more and more critical, and if not treated smartly this issue will negatively affect drivers by wasting time and fuel gas while waiting for hours in lanes. This paper presents a new and smart way to mitigate this issue in an affordable cost, minimum processing power, and low power consumption. This concept takes into consideration the majority of the cases that may cause congestion and presents a smart and accurate outputs to ease traffic flow leading to the prediction of the peak hours of traffic congestion for smarter control. A model is designed to study the case of a four lanes crossroad with two traffic lights and two LCD monitors. The strategy in reading data is divided into two parts: real data from sensors and pre collected data from google maps to create a kind of a predicted pattern over a certain time interval. The responsiveness of the system is analyzed thoroughly, and the accuracy of all possible cases is carefully considered and evaluated. Each part of the system was tested alone, and the overall system is still in an ongoing testing phase. The results have shown minimum faulty errors and accepted outputs that can lead to safe traffic control decisions. Finally, integrating more IoT devices and sensors between V2V, V2P, V2I with the help of artificial intelligence will definitely optimize this system with higher accuracy.

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Design and Implementation of a QoL Improvement System for Visually Impaired Patients

Design and Implementation of a QoL Improvement System for Vi...

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International Conference on Advances in Computational Tools for engineering Applications, ACTEA

作者： Roy Abi Zeid Daou Jad Beaino Johnny Hawat Ramy Ibrahim Ali Hayek Josef Boercsoek Jose Javier Serrano Olmedo Mechatronics department Faculty of Engineering Université La Sagesse Beirut Lebanon MART Learning Education and Research Center Chananir Lebanon Department Lippstadt 2 Hardware Engineering University of Applied Science Hamm-Lippstadt Hamm Department of Computer Architecture and System Programming Faculty of Electrical Engineering/Computer Science University of Kassel Germany Lab of Bioinstrumentation and Nanomedicine Centro de Tecnología Biomédica Universidad Politécnica de Madrid Spain

Visually impaired people encounter several challenges in their mobility and navigation. Their daily activities are obstructed due to their inability to adapt or recognize accurately their surroundings, especially outside their house which they are more familiar with. Thus, it becomes the main reason of accidents, falling off, getting lost in unknown areas, etc. Furthermore, one of the sensory systems that helps the body to process data about the external environment is the visual system. Blind people also lose touch with the outside world, develops poor motor habits, which results in postural problems as a result. This project will assist visually impaired people in their daily life and simplify normal tasks through a system combining two previously designed projects, “Smart Shoes for Blind and Visually Impaired People”, and “Human posture monitoring device”. The multifunctional system is developed with the goal of securing safe movements for visually impaired people as well as maintaining a good back posture by detecting leaning postures (LP). The purpose of the smart shoe is to identify obstacles and protect the user from unintended accidents. A compatible Android application has been developed to alert the user when there is an obstruction or when he is walking on a wet surface. Voice alarms will be used to acoustically alert the user. If the user collapses, a message with their position will be sent right away to a relative. On the other hand, the smart vest will identify the position of the user's back and alert him to maintain a straight posture through the same application as well. As the system is dealing with human health, some safety measurements would be taken into consideration to implement a safe electrical system in order to reduce error and to increase accuracy.

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Storage of key Features of Images of Sulfur Imprints in the Mathematical Structure Form

Storage of key Features of Images of Sulfur Imprints in the ...

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2020 International Multi-Conference on Industrial engineering and Modern Technologies, FarEastCon 2020

作者： Bagaev, I.I. Logunova, O.S. Institute of Energy and Automated Systems Nosov Magnitogorsk State Technical University Department of Computer Engineering and Programming Magnitogorsk Russia

ISBN: (纸本)9781728169514

The aim of the study is to develop a method for storing images of sulfur imprints. In the framework of the study, a more effective method of representing images of sulfur imprints by using knowledge of the specificity of these images was developed compared to universal methods. In particular, a method for storing images of sulfur imprints has been developed, which stores information about objects that are significant for technological analysis in the image. The developed method was tested for images of sulfur imprints of continuously cast billets for the conditions of a large metallurgical enterprise in Russia. To conduct a computational experiment, a software product was designed and developed that allows you to convert the image of a sulfur imprint into a mathematical representation, as well as the process of restoring the main image. Using the array of retrospective information of 2011-2018 and the developed software product, the calculation of the effectiveness of the presentation of graphic information in the form of a structural unit of information without directly saving the image was performed. The average share of stored information from the original volume is 0.53 percent © 2020 IEEE.

关键词： Sulfur

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Speaker's Emotions Recognition Module Based on the GoogleLeNet Neural Network

Speaker's Emotions Recognition Module Based on the GoogleLeN...

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2022 International Conference on Smart Information Systems and Technologies, SIST 2022

作者： Tereikovskyi, Ihor Tereikovska, Liudmyla Kryvoruchko, Olena Tereikovskyi, Oleh Tyshchenko, Dmytro Franchuk, Tamara National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of System Programming And Specialized Computer Systems Kyiv Ukraine Kyiv National University Of Construction And Architecture Dept. Of Information Technology For Design And Applied Mathematics Kyiv Ukraine Kyiv National University Of Trade And Economics Dept. Of Software Engineering And Cyber Security Kyiv Ukraine National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of Department Of Computer Engineering Kyiv Ukraine

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

The article is devoted to the development of means for recognition of the emotions of the speaker, based on the neural network analysis of fixed fragments of the voice signal. The possibility of improving recognition tools through the use of a convolutional neural network of the GoogLeNet type is shown. The expediency of training the network on the examples of the TESS database is determined. A procedure for representing a voice signal in the form of a square grayscale image has been developed. Based on the use of the specified database, the modification of the GoogLeNet network was carried out. The architecture and software implementation of the recognition module have been developed. With the help of computer experiments, it has been established that after 150 epochs of training on a fairly limited training sample, the modified GoogLeNet makes it possible to achieve an accuracy of recognition of the speaker's emotions of approximately 0.97 on test examples. The obtained accuracy is comparable to the accuracy of the best modern systems of this purpose and confirms the promise of using neural network models like GoogLeNet for emotion recognition based on voice signal analysis. The need for further research aimed at reducing the resource intensity of the neural network model is shown. © 2022 IEEE.

关键词： Emotion Recognition

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Design of Intelligent Decision Support System With Ensembled Machine Learning to Predict Students' Performance

Design of Intelligent Decision Support System With Ensembled...

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Communication, computer Sciences and engineering (IC3SE), International Conference on

作者： S Deepan Dineshkumar Rajendran Navdeep Dhaliwal K Praveena Melanie Lourens Anandhi R J School of CS & AI SR University Warangal Telangana India Computer Games Design and Programming School of Computing and Innovative Technologies British University Vietnam Vietnam Lovely Professional University Phagwara India Institute of Aeronautical Engineering Dundigal Hyderabad Deputy Dean Faculty of Management Sciences Durban University of Technology South Africa Department of Information Science Engineering New Horizon College of Engineering Bangalore

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

Evaluating student performance is important for universities and institutions in the current student education landscape because it helps them create models that work better for students. The automation of various features related to fundamental student traits and behaviours that manage massive amounts of data efficiently processes these. To handle student records that included information about students' behaviour and how it related to their academic performance, the companies employed models of classification with mining concepts. Additionally, the quality of result classification can be substantially improved by using learning analytics and Educational Data Mining (EDM). The educational establishments are making an effort to lower the low student performance. To address this issue, numerous methods for assessing student performance have been devised, allowing the relevant faculties to intervene and enhance the final product. Three classes—Low Performance Student, Average Student, and Smart Student—were created using the K-Mean Clustering methodology for classifying student records. Features including grade point, number of deficits, student attendance, medium of education, and board of education are taken into account when classifying the data. In this case, the WEKA tool is also utilized for implementing the model and outcome assessments.

关键词： Deep learning Decision support systems Analytical models Data analysis Automation Education Companies

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